icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2016-17 | Toronto Furies | CWHL | 24 | 6 | 8 | 14 | 0.583 | — | — | — | — |
| 2017-18 | MoDo Hockey | SDHL | 36 | 25 | 30 | 55 | 1.528 | 1.7868 | 1.7058 | — | — |
| 2018-19 | MoDo Hockey | SDHL | 36 | 27 | 37 | 64 | 1.778 | 2.0791 | 1.9059 | — | — |
| 2019-20 | Brynäs IF | SDHL | 36 | 23 | 22 | 45 | 1.250 | 1.4619 | 1.4619 | — | — |
| 2020-21 | Luleå HF | SDHL | 36 | 29 | 37 | 66 | 1.833 | 2.1440 | 2.1440 | — | — |
| 2021-22 | Toronto Six | PHF | 11 | 4 | 3 | 7 | 0.636 | — | — | — | — |
| 2022-23 | Toronto Six | PHF | 24 | 9 | 12 | 21 | 0.875 | — | — | — | — |
| 2023-24 | Toronto Six | PHF | 24 | 9 | 12 | 21 | 0.875 | — | — | — | — |
| 2024-25 | Minnesota Frost | PWHL | 30 | 9 | 10 | 19 | 0.633 | — | — | — | — |
| 2025-26 | — | PWHL | 30 | 1 | 8 | 9 | 0.300 | — | — | — | — |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2015-16 | Minnesota Duluth | D1 | WCHA-W | SR | 37 | 19 | 19 | 38 | 1.027 |
| 2014-15 | Minnesota Duluth | D1 | WCHA-W | JR | 32 | 3 | 3 | 6 | 0.188 |
| 2013-14 | UConn | D1 | HEA-W | SO | 35 | 10 | 17 | 27 | 0.771 |
| 2012-13 | UConn | D1 | HEA-W | FR | 34 | 10 | 7 | 17 | 0.500 |
How to read this: NCAAe and D3e factors convert a player's junior PPG into expected NCAA scoring at the D1 or D3 level. Harder conferences → lower projected PPG for the same player. A strong junior player (e.g. USHL 0.90 PPG) will project much higher in NESCAC than Big Ten because the D3 scoring environment is lower-difficulty.
Strength factor: conferences above 1.0 are harder than average; below 1.0 are easier. The formula is: Base NCAAe PPG ÷ Conference Strength = Projected PPG.